Water pretreatment method and anaerobic ammonium oxidation water treatment process

Abstract

The present application provides a water pretreatment method and an anaerobic ammonia oxidation water treatment process, wherein the water pretreatment method includes: obtaining the actual amount of water currently inflowing into the water pretreatment system and the current water quality grayscale value characterizing the comprehensive water quality of the current inflowing water; using the actual amount of water and the current water quality grayscale value to determine the operating parameters of the water pretreatment system, specifically including: in response to determining that the actual amount of water is greater than a first threshold, the water pretreatment system operates with a first parameter; in response to determining that the actual amount of water is less than a second threshold, the water pretreatment system operates with a second parameter; in response to determining that the actual amount of water is less than or equal to the first threshold and greater than or equal to the second threshold, using the current water quality grayscale value to perform an operation decision step to determine the operating parameters. In the present disclosure, the water pretreatment system timely adjusts the operating parameters according to the amount of water and water quality of the inflowing water, effectively reducing the operating cost, slowing down the pollution rate of the filter component, and increasing the service life of the filter component.

Description

Technical Field

[0001] The present application relates to the field of water treatment technology, and in particular to a water pretreatment method and an anaerobic ammonia oxidation water treatment process. Background Art

[0002] Existing water pretreatment systems usually do not adjust operating parameters and always operate with constant operating parameters. However, in actual applications, the water quality and water quantity of the water pretreatment system will change to a certain extent, and the reasonable operating parameters corresponding to different water qualities and water quantities are different. In the face of fluctuations in the quality and quantity of incoming water, the existing water pretreatment system still operates with the original constant operating parameters, which has unreasonable operation problems, resulting in problems such as the inability to process the incoming water in a timely manner, low production efficiency, and high operating costs; unreasonable operating parameters will further increase the degree of contamination of the filter components of the water pretreatment system, reduce the service life of the filter components, and increase the operating costs of the pretreatment process. Summary of the invention

[0003] In view of this, the purpose of this application is to propose a water pretreatment method and an anaerobic ammonia oxidation water treatment process to solve the above technical problems.

[0004] In a first aspect of the present application, a water pretreatment method is provided, comprising:

[0005] Obtain the actual water volume of the current influent water pretreatment system and the current water quality grayscale value representing the current comprehensive water quality of the influent water;

[0006] Determining the operating parameters of the water pretreatment system using the actual water volume and the current water quality grayscale value specifically includes:

[0007] In response to determining that the actual water volume is greater than a first threshold, the water pre-treatment system operates with a first parameter;

[0008] In response to determining that the actual water volume is less than a second threshold, the water pretreatment system operates with a second parameter;

[0009] In response to determining that the actual water volume is less than or equal to the first threshold and greater than or equal to the second threshold, an operation decision step is performed using the current water quality grayscale value; the operation decision step includes:

[0010] Determine the water quality grade of the current water inflow of the water pretreatment system by using the current water quality gray value to obtain the current water quality grade; wherein the water quality grade is pre-divided according to the water quality gray value representing the comprehensive water quality situation, including the first grade, the second grade and the third grade representing the water quality situation getting worse in sequence;

[0011] In response to determining that the current water quality level is the first level, the water pretreatment system operates with a third parameter;

[0012] In response to determining that the current water quality level is the second level, the water pretreatment system operates with fourth parameters;

[0013] In response to determining that the current water quality level is the third level, the water pretreatment system operates with a fifth parameter;

[0014] Among them, the operating parameters include operating flux, the operating flux corresponding to the first parameter is greater than the operating flux corresponding to the second parameter, and the operating flux corresponding to the third parameter, the operating flux corresponding to the fourth parameter, and the operating flux corresponding to the fifth parameter increase sequentially.

[0015] Furthermore, the current water quality grayscale value is calculated based on the water quality data of the current influent of the water pretreatment system, specifically including:

[0016] Acquire water quality data of current influent of the water pretreatment system, wherein the water quality data includes detection data of multiple types of water quality indicators, and obtain parameters of the current multiple types of water quality indicators;

[0017] The parameters of the current multiple types of water quality indicators are respectively standardized in a preset range by using the maximum and minimum method to obtain the standardized parameters of the current multiple types of water quality indicators;

[0018] The current water quality grayscale value is calculated based on the preset weight parameters of each type of water quality index and the standardized parameters of the current multiple types of water quality indexes;

[0019] The current water quality grayscale value is calculated by the following formula:

[0020]

[0021] Among them, y 0 is the current water quality grayscale value, n is the number of categories of water quality indicators, i∈[1,n], w i is the weight parameter of the i-th water quality index, X norm_i It is the standardized parameter of the current i-th category water quality indicator.

[0022] Furthermore, the current water quality grayscale value is calculated by using a pre-built grayscale value prediction model using water quality data of the influent before a preset time step of the water pretreatment system, specifically including:

[0023] Acquire water quality data of influent water of the water pretreatment system before a preset time step, wherein the water quality data includes detection data of multiple types of water quality indicators, and obtain parameters of the multiple types of water quality indicators before the preset time step;

[0024] Inputting the parameters of the multiple types of water quality indicators before the preset time step into the gray value prediction model to predict the current water quality gray value;

[0025] The gray value prediction model is constructed using a long short-term memory neural network based on a sliding window, and specifically includes:

[0026] Obtain water quality data of water inflow at multiple historical moments of the water pretreatment system and corresponding water quality grayscale values ​​at multiple historical moments, and record corresponding time series information to obtain training data;

[0027] Processing the training data using a sliding window method based on a preset sliding window step size and a preset sliding window length to obtain a training sample set;

[0028] Using the training sample set to perform prediction training on an initial model constructed based on a long short-term memory neural network based on a preset time step until a preset termination condition is reached;

[0029] The model parameters at the time when the training is terminated are determined as final model parameters, and the final model parameters are applied to the initial model to obtain the gray value prediction model.

[0030] Furthermore, the operating flux corresponding to the first parameter and the operating flux corresponding to the second parameter are calculated by the following formula: 0 =F×Q 1 / Q 0 ;

[0031] The operating flux corresponding to the third parameter is 0.7F to 0.9F, the operating flux corresponding to the fourth parameter is F, and the operating flux corresponding to the fifth parameter is 1.2F to 1.4F;

[0032] Among them, F 0 is the operating flux corresponding to the first parameter or the operating flux corresponding to the second parameter, F is the design flux of the water pretreatment system, Q 1 is the actual water volume, Q 0 The designed inlet water volume for the water pretreatment system.

[0033] Furthermore, the operating parameters also include cleaning frequency; the cleaning frequency corresponding to the first parameter is greater than the cleaning frequency corresponding to the second parameter, and the cleaning frequency corresponding to the third parameter, the cleaning frequency corresponding to the fourth parameter, and the cleaning frequency corresponding to the fifth parameter increase sequentially.

[0034] Furthermore, the cleaning frequency corresponding to the first parameter and the cleaning frequency corresponding to the second parameter are calculated by the following formula: N 0 =1440 / T-60000Q1 / FAT;

[0035] The cleaning frequency corresponding to the third parameter is 0.7N-0.9N, the cleaning frequency corresponding to the fourth parameter is N, and the cleaning frequency corresponding to the fifth parameter is 1.2N-1.4N;

[0036] Among them, N 0 is the cleaning frequency corresponding to the first parameter or the cleaning frequency corresponding to the second parameter, T is the single cleaning time, Q 1 is the actual water volume, F is the design flux of the water pretreatment system, A is the total filtration area of ​​the water pretreatment system, and N is the design cleaning frequency of the water pretreatment system.

[0037] Furthermore, the multiple types of water quality indicators are determined according to the type of water entering the water pretreatment system;

[0038] When the water inlet of the water pretreatment system is tap water, the multiple water quality indicators include water temperature, COD, turbidity and TOC; when the water inlet of the water pretreatment system is municipal sewage, the multiple water quality indicators include water temperature, COD, ammonia nitrogen and turbidity; when the water inlet of the water pretreatment system is anaerobic ammonia oxidation treated water, the multiple water quality indicators include water temperature, COD, ammonia nitrogen and SS.

[0039] Furthermore, the water quality grade is obtained according to the following steps:

[0040] Acquire water quality data of water entering the water pretreatment system at multiple historical moments, wherein the water quality data includes detection data of multiple types of water quality indicators, and obtain parameters of the multiple types of water quality indicators at multiple historical moments;

[0041] Based on the maximum and minimum value method, the parameters of the multiple types of water quality indicators at the multiple historical moments are respectively standardized in the preset intervals to obtain the standardized parameters of the multiple types of water quality indicators at the multiple historical moments;

[0042] The weight parameters of each type of water quality index are determined based on the importance of water quality index using the analytic hierarchy process.

[0043] Calculate the water quality grayscale value at each historical moment by using the weight parameter of each type of water quality index and the standardized parameters of multiple types of water quality index at each historical moment, and obtain the water quality grayscale values ​​at multiple historical moments;

[0044] Determine the grouping breakpoints of the water quality grayscale values ​​at the multiple historical moments using the natural breakpoint method based on the number of water quality grade classifications to obtain a first breakpoint A and a second breakpoint B;

[0045] Determine that the water quality grayscale value ranges corresponding to the first level, the second level, and the third level are [0, A), [A, B), and [B, 1] respectively.

[0046] Further, the water pretreatment system is a membrane treatment system;

[0047] Wherein, the membrane treatment system is a ceramic membrane water treatment system.

[0048] In a second aspect of the present application, an anaerobic ammonium oxidation water treatment process is provided, wherein the water to be treated is pretreated using a water pretreatment method described in the first aspect, and the pretreated water is passed into an anaerobic ammonium oxidation system for treatment.

[0049] From the above, it can be seen that the present application provides a water pretreatment method and an anaerobic ammonia oxidation water treatment process. The operating parameters of the water pretreatment system are first determined according to the actual water volume currently entering the water pretreatment system. When the actual water volume is greater than the first threshold, it means that the current incoming water volume has increased significantly. At this time, the system is operated with a larger first parameter to ensure that the current incoming water can be processed in time. When the actual water volume is less than the second threshold, it means that the front-end incoming water volume is too small. At this time, the water pretreatment system is operated with a smaller second parameter, which can reduce the system load, reduce operating costs, and increase the service life of the filter component. When the actual water volume is less than or equal to the first threshold and greater than or equal to the second threshold, it means that although the front-end water volume fluctuates, the fluctuation range is small. At this time, the operating parameters of the water pretreatment system are further determined in combination with the water quality of the incoming water. When the current water quality level is the first level, it means that the water quality of the incoming water is good. At this time, it is operated with a smaller third parameter, which is friendly to the filter components of the water pretreatment system. The pollution rate of the filter components is low, which can effectively increase the service life of the filter components, and the cost increase is not obvious. When the current water quality level is the third level, it means that the water quality of the current incoming water is poor. At this time, it is operated with a higher fifth parameter to shorten the processing time and reduce energy consumption costs. When the current water quality level is the second level, it means that the water quality of the current incoming water is in line with normal conditions. Although the water quality fluctuates, the fluctuation range is small. At this time, it is operated with a fourth parameter between the third parameter and the fifth parameter. Thereby, the operating parameters of the water pretreatment system can be adjusted in time according to the water volume and water quality of the water pretreatment system, so that the water pretreatment system can be adjusted to a reasonable operating flux in time when facing different water volumes and water qualities, thereby realizing flexible adjustment of the operating flux of the water pretreatment system and effectively coping with the unstable water supply at the front end. It can not only effectively meet production needs, but also reduce operating costs. At the same time, it can effectively slow down the pollution rate of the filter components and increase the service life of the filter components, which is conducive to practical application and wide promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 This is a flow chart of the water pretreatment method in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0053] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0054] Water pretreatment usually refers to the process of preliminarily treating the incoming water before deep or precision water treatment to improve the subsequent deep or precision water treatment effect. In the wastewater treatment process, the process before entering the biochemical treatment is generally called pretreatment. Commonly used pretreatment technical means include setting grids, grit chambers, primary sedimentation tanks, flotation tanks, etc., thereby removing large pollutants, mud, suspended matter, sludge and other solid pollutants in the wastewater in advance. In the water pretreatment process, coagulants can also be added as auxiliary agents to condense and flocculate the colloidal particles in the water into large particles, so as to remove them through sedimentation, filtration, etc. After the water pretreatment process is completed, it will enter the corresponding deep or precision water treatment system to deeply treat some difficult to degrade, harmful, toxic and other substances to achieve the standard of drainage water quality.

[0055] A water pretreatment system is a device used to achieve pretreatment, which is usually equipped with a water inlet, a water outlet, a filter component, etc. According to the selection of the filter component, it can achieve the separation of various substances such as solid-liquid, liquid-liquid, gas-liquid, etc., such as membrane treatment systems, ceramic membrane water treatment systems, etc., which are all commonly used water pretreatment systems. Existing water pretreatment systems usually operate with constant operating parameters, and important parameters such as operating flux are generally not adjusted. Then in actual applications, the water quality and water volume of the water pretreatment system will have certain changes, and the reasonable operating parameters corresponding to different water qualities and water volumes are different. However, the existing water pretreatment system still uses the original constant operating parameters to operate, and there is a problem of unreasonable operating parameters. When the amount of incoming water is small, operating with the original operating parameters will add unnecessary operating burden to the system and increase operating costs; when the amount of incoming water is large, the original operating parameters are usually difficult to meet production needs, resulting in the inability to process the incoming water in a timely manner. In the process of water pretreatment, taking the membrane treatment system as an example, as the treatment time increases, the transmembrane pressure difference will gradually increase, but when treating different water qualities, there are obvious differences in the growth rate of the transmembrane pressure difference. When the water quality is poor, the transmembrane pressure difference increases significantly with the extension of the treatment time. The longer the time, the greater the increase. When the water quality is good, although the transmembrane pressure difference increases, the increase rate and amplitude are significantly smaller than when the water quality is poor and normal, and the transmembrane pressure difference is positively correlated with the energy consumption of the system. Therefore, for water with poor water quality, if the water pretreatment system is still operated with the original operating parameters, the transmembrane pressure difference will increase rapidly in the later stage of treatment, which will lead to a significant increase in costs. At the same time, unreasonable operation will further increase the degree of contamination of the filter components of the water pretreatment system, bringing about the problem of reducing the service life of the filter components. The above problems are particularly obvious in the process of treating anaerobic ammonia oxidation water to be treated. Anaerobic ammonium oxidation process refers to the biological oxidation process in which anaerobic ammonium-oxidizing bacteria use ammonium ions as electron donors and nitrite nitrogen as electron acceptors to convert ammonia nitrogen into nitrogen gas under anaerobic conditions. It is mainly used to treat wastewater with high concentrations of ammonia nitrogen, such as food digester, sludge nitrification supernatant, garbage filtrate, and leather wastewater. The anaerobic ammonium oxidation process usually includes a water pretreatment stage and an anaerobic ammonium oxidation reaction stage. In the actual application of the anaerobic ammonium oxidation process, when industrial wastewater such as food digester, garbage filtrate, sludge nitrification supernatant, and leather wastewater enter the pretreatment stage as anaerobic ammonium oxidation treated water, it usually has the characteristics of obvious fluctuations in water quantity and water quality, which leads to a variety of problems such as the overall anaerobic ammonium oxidation process treatment efficiency, high operating cost, and unfavorable practical application and promotion.

[0056] Based on the above situation, the present disclosure provides a water pretreatment method, such as Figure 1 As shown, it is applied to the water pretreatment system, and can timely adjust the operating parameters according to the water volume and water quality of the water pretreatment system, improve production efficiency, reduce operating costs, and extend the service life of the system, including:

[0057] Obtain the actual water volume of the current influent water pretreatment system and the current water quality grayscale value representing the current comprehensive water quality of the influent water;

[0058] Determining the operating parameters of the water pretreatment system using the actual water volume and the current water quality grayscale value specifically includes:

[0059] In response to determining that the actual water volume is greater than a first threshold, the water pre-treatment system operates with a first parameter;

[0060] In response to determining that the actual water volume is less than a second threshold, the water pretreatment system operates with a second parameter;

[0061] In response to determining that the actual water volume is less than or equal to the first threshold and greater than or equal to the second threshold, an operation decision step is performed using the current water quality grayscale value; the operation decision step includes:

[0062] Determine the water quality grade of the current water inflow of the water pretreatment system by using the current water quality gray value to obtain the current water quality grade; wherein the water quality grade is pre-divided according to the water quality gray value representing the comprehensive water quality situation, including the first grade, the second grade and the third grade representing the water quality situation getting worse in sequence;

[0063] In response to determining that the current water quality level is the first level, the water pretreatment system operates with a third parameter;

[0064] In response to determining that the current water quality level is the second level, the water pretreatment system operates with fourth parameters;

[0065] In response to determining that the current water quality level is the third level, the water pretreatment system operates with a fifth parameter;

[0066] Among them, the operating parameters include operating flux, the operating flux corresponding to the first parameter is greater than the operating flux corresponding to the second parameter, and the operating flux corresponding to the third parameter, the operating flux corresponding to the fourth parameter, and the operating flux corresponding to the fifth parameter increase sequentially.

[0067] The amount of water coming into a water plant usually has a certain degree of volatility. In order to cope with this fluctuation, a water reservoir or regulating tank is usually set up. When the amount of water coming in is large, the water can be stored temporarily, thereby reducing the workload of the subsequent related treatment systems. However, the storage capacity of the water reservoir or regulating tank is limited, and the water cannot be stored indefinitely. At the same time, the complete process of water treatment is a continuous and complex process. The previous process will have a potential impact on the subsequent process, and the effluent of the water pretreatment system will also affect the subsequent deep water treatment process.

[0068] In the present method, the operating parameters of the water pretreatment system are first determined according to the actual amount of water currently entering the water pretreatment system. When the actual water amount is greater than the first threshold, it indicates that the current amount of incoming water has increased significantly. At this time, the water pretreatment system operates with a larger first parameter to ensure that the current incoming water can be processed in time. When the actual water amount is less than the second threshold, it indicates that the amount of incoming water at the front end is too small. At this time, operating the water pretreatment system with a smaller second parameter can effectively meet the current incoming water demand, while also reducing the system load, reducing operating costs, and increasing the service life of the filter components. When the actual water volume is less than or equal to the first threshold and greater than or equal to the second threshold, it means that although the water volume of the front-end water fluctuates, the fluctuation range is small. At this time, it is necessary to further combine the water quality of the incoming water to determine the operating parameters of the water pretreatment system, that is, use the current water quality gray value to execute the operation decision step to determine the operating parameters of the water pretreatment system; when the current water quality level is the first level, it means that the water quality of the incoming water is good. At this time, it is operated with a smaller third parameter, which is friendly to the filter component of the water pretreatment system. The pollution rate of the filter component is low, which can effectively increase the service life of the filter component, and the cost increase is not obvious; when the current water quality level is the third level, it means that the water quality of the current incoming water is poor. The cost of poor water quality increases significantly with the extension of the processing time. Therefore, at this time, the water pretreatment system is operated with the operating flux corresponding to the higher fifth parameter to shorten the processing time and reduce the energy consumption cost; when the current water quality level is the second level, it means that the water quality of the current incoming water meets the general normal situation. Although the water quality fluctuates, the fluctuation range is small. At this time, the water pretreatment system operates with a fourth parameter between the third parameter and the fifth parameter. The operating flux is one of the core indicators of the water pretreatment system, which is used to characterize the filtering capacity of the water pretreatment system. When other conditions remain unchanged, the larger the operating flux, the more water the water pretreatment system can process. Reasonable operating flux can effectively reduce the pollution rate of the filter component and improve the service life of the filter component. In practical applications, the operating flux is affected by factors such as water temperature, pH, operating pressure, flow rate, solid particle content, and metal content. In the water pretreatment system, the operating flux can usually be adjusted by adjusting the frequency of the frequency converter. By adjusting the frequency of the frequency converter, the flow rate, pressure and other parameters of the water pump can be adjusted to adjust the operating flux. In this method, the operating parameters can be adjusted in time according to the water volume and water quality of the water pretreatment system, so that the water pretreatment system can be adjusted to a reasonable operating flux in time when facing different water volume and water quality conditions, realizing the flexible adjustment of the operating flux of the water pretreatment system, effectively coping with the fluctuation of the incoming water volume and water quality, which can not only effectively meet production needs, but also reduce operating costs, and at the same time effectively slow down the pollution rate of the filter component, improve the service life of the filter component, which is conducive to practical application and wide promotion.

[0069] In some embodiments, the current water quality grayscale value is calculated based on the water quality data of the current influent of the water pretreatment system, specifically including:

[0070] Acquire water quality data of current influent of the water pretreatment system, wherein the water quality data includes detection data of multiple types of water quality indicators, and obtain parameters of the current multiple types of water quality indicators;

[0071] The parameters of the current multiple types of water quality indicators are respectively standardized in a preset range by using the maximum and minimum method to obtain the standardized parameters of the current multiple types of water quality indicators;

[0072] The current water quality grayscale value is calculated based on the preset weight parameters of each type of water quality index and the standardized parameters of the current multiple types of water quality indexes;

[0073] The current water quality grayscale value is calculated by the following formula:

[0074]

[0075] Among them, y 0 is the current water quality grayscale value, n is the number of categories of water quality indicators, i∈[1,n], w i is the weight parameter of the i-th water quality index, X norm_i It is the standardized parameter of the current i-th category water quality indicator.

[0076] Water quality indicators represent the types and quantities of impurities in water and are specific measures for judging the degree of water pollution. There are many types of water quality indicators, which can be divided into sensory physical performance indicators, other physical water quality indicators, chemical water quality indicators and biological water quality indicators. Sensory physical performance indicators include temperature, chromaticity, turbidity, transparency, etc. Other physical water quality indicators include total solids, suspended solids, fixed solids, conductivity, etc. Chemical water quality indicators include pH value, hardness, alkalinity, various ion concentrations, general organic matter concentrations, etc. Biological water quality indicators generally include total bacteria count, total coliform count, various pathogenic bacteria, viruses, etc. In actual application, it is usually difficult to evaluate the water quality well by using a single water quality indicator. Therefore, in this method, multiple types of water quality indicators are used to comprehensively calculate the current water quality gray value that characterizes the comprehensive water quality of the current influent, which can effectively evaluate the water quality of the current influent of the water pretreatment system, thereby realizing the precise regulation of the operating parameters of the water pretreatment system.

[0077] In this method, by setting up detection instruments, detection probes and other equipment to detect the water quality of the current water inlet of the water pretreatment system in real time, the detection data of multiple types of water quality indicators are obtained, that is, the parameters of the current multiple types of water quality indicators are obtained. Since the data of different water quality indicators vary greatly, after obtaining the parameters of the current multiple types of water quality indicators, the maximum and minimum method is used to standardize the parameters of the current multiple types of water quality indicators in a preset range to obtain the standardized parameters of the current multiple types of water quality indicators. The maximum and minimum method refers to the use of the maximum and minimum values ​​in the data column for standardization, and the standardized value is between [0,1]. Optionally, the preset interval can be set to [0,1], and the maximum and minimum values ​​can be determined based on the historical water quality data processed by the water pretreatment system. After obtaining the standardized parameters of the current multiple types of water quality indicators, the parameters of each type of water quality indicator are standardized to [0,1], so that the multiple types of water quality indicators are comparable and calculable. The specific calculation formula for standardization using the maximum and minimum method is as follows:

[0078]

[0079] X norm It is the standardized parameter of water quality index X, X.min is the minimum value of water quality index X, and X.max is the maximum value of water quality index X.

[0080] In practical applications, different types of water quality indicators have different degrees of influence on the overall water quality. Therefore, it is necessary to calculate the current water quality grayscale value based on the pre-set weight parameters of each type of water quality indicator and the standardized parameters of the current multiple types of water quality indicators to characterize the comprehensive water quality of the current influent of the water pretreatment system. Optionally, the pre-set weight parameters of each type of water quality indicator can be determined using the hierarchical analysis method. Finally, the current water quality grayscale value is calculated based on the following formula:

[0081]

[0082] Among them, y 0 is the current water quality grayscale value, n is the number of categories of water quality indicators, i∈[1,n], w i is the weight parameter of the i-th water quality index, X norm_i It is the standardized parameter of the current i-th category water quality indicator.

[0083] In the above process, the real-time collection of the water quality data of the current influent of the water pretreatment system is realized through the detection equipment, and the current water quality grayscale value can be further calculated in real time. According to the current water quality grayscale value calculated in real time, the operating parameters of the water pretreatment system can be adjusted in real time to effectively cope with the fluctuations in the influent water volume and water quality; at the same time, multiple types of water quality indicators are used to characterize the comprehensive water quality situation to avoid the problems of missing and inaccurate evaluation caused by a single water quality indicator. The current water quality grayscale value obtained is more in line with the actual water quality of the current influent, and is used to adjust the operating parameters of the water pretreatment system. The adjusted operating parameters are more in line with the actual needs of the current influent water quality, thereby making the adjustment of the operating parameters more accurate.

[0084] In some embodiments, the current water quality grayscale value is calculated by a pre-built grayscale value prediction model using the water quality data of the influent before a preset time step of the water pretreatment system, specifically including:

[0085] Acquire water quality data of influent water of the water pretreatment system before a preset time step, wherein the water quality data includes detection data of multiple types of water quality indicators, and obtain parameters of the multiple types of water quality indicators before the preset time step;

[0086] Inputting the parameters of the multiple types of water quality indicators before the preset time step into the gray value prediction model to predict the current water quality gray value;

[0087] The gray value prediction model is constructed using a long short-term memory neural network based on a sliding window, and specifically includes:

[0088] Obtain water quality data of water inflow at multiple historical moments of the water pretreatment system and corresponding water quality grayscale values ​​at multiple historical moments, and record corresponding time series information to obtain training data;

[0089] Processing the training data using a sliding window method based on a preset sliding window step size and a preset sliding window length to obtain a training sample set;

[0090] Using the training sample set to perform prediction training on an initial model constructed based on a long short-term memory neural network based on a preset time step until a preset termination condition is reached;

[0091] The model parameters at the time when the training is terminated are determined as final model parameters, and the final model parameters are applied to the initial model to obtain the gray value prediction model.

[0092] Long Short-Term Memory (LSTM) neural network is a time-recurrent neural network and a special recursive neural network that can analyze input using time series. In the LSTM neural network, by introducing the gating mechanism and cell state, it is possible to retain information over a longer time span, thereby capturing long-term dependencies. The forget gate is used to selectively forget long-term and useless information, thereby effectively transmitting and expressing information in long time series without causing useful information from a long time ago to be ignored. It is currently used in natural language processing, time series prediction, and many other fields. For example, it uses the contextual information in the text to predict the next word, understands the overall semantics of the text to judge the sentiment tendency, and makes future stock price and weather forecasts.

[0093] The sliding window method is used to solve the problem of finding the properties of continuous intervals that meet certain conditions. It refers to using a set of fixed-length windows to slide from the starting point to the end point of a long sequence, and recording the value in the window every time it slides forward. By increasing dimensions, setting default values, and removing old values ​​in a timely manner, the one-dimensional horizontal long sequence can be expanded into a vertical superposition of several horizontal short sequences while retaining information relatively completely, and the change in the horizontal length of the original sequence can be converted into a change in the number of vertical samples, thereby fixing the depth of the feature extractor in the time series and being able to handle input data of any length.

[0094] In practical applications, through 0It takes a certain amount of time to calculate the current water quality grayscale value of the current water inflow of the water pretreatment system in real time using the calculation formula. After obtaining the current water quality grayscale value, it also takes a certain amount of time to adjust the operating parameters of the water pretreatment system. Generally speaking, water quality changes with time with a certain regularity. Mining the time series law of historical water quality data and predicting water quality in the short term in the future have a certain positive effect on realizing the regulation of the water pretreatment system. Taking anaerobic ammonia oxidation treated water as an example, generally speaking, its water quality and water volume show relatively regular fluctuations over time. Therefore, in order to further improve the operating accuracy and timeliness of this method, a grayscale value prediction model constructed by a long short-term memory neural network based on a sliding window can be used to predict the current water quality grayscale value. During the model training process, the historical data of the water pretreatment system is used to train the model, and the mapping relationship between multiple types of water quality indicators and water quality grayscale values ​​is established in the time series. The characteristics of the water quality situation in the time series are deeply mined, thereby realizing the prediction of water quality grayscale values. When the grayscale value prediction model obtained after training is used for practical application, the grayscale value prediction model uses the water quality data of the influent before the preset time step of the water pretreatment system to obtain the current water quality grayscale value in advance, and then the operating parameters of the water pretreatment system can be adjusted in time; at the same time, the training sample set is obtained after processing using the sliding window method, and using it for model training can effectively improve the accuracy of the model, and the obtained grayscale value prediction model has higher accuracy, and the predicted current water quality grayscale value is also more accurate. Specifically, the preset termination condition can be determined according to the maximum number of iterations or the model accuracy requirements, that is, when the prediction training reaches the maximum number of iterations or the model accuracy reaches the preset requirements, the prediction training is considered to be completed, and the current model parameters are applied to the initial model to obtain the grayscale value prediction model.

[0095] In some embodiments, the preset time step can be 5 to 15 minutes, specifically 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 minutes, which can be adjusted according to the characteristics of the water pretreatment system inlet, or according to the operating characteristics of the water pretreatment system in actual application, or the preset time step can be set to less than 5 minutes or greater than 15 minutes, without specific restrictions. Taking the preset time step of 10 minutes as an example, when the gray value prediction model is predicting, the water quality gray value after 10 minutes can be predicted. In other words, the current water quality gray value is not calculated based on the real-time water quality data of the current water pretreatment system inlet, but is predicted by the water quality data of the water inlet 10 minutes ago (i.e., before the preset time step). The relevant staff or the automatic adjustment system can know the water quality of the water to be treated 10 minutes in advance, so that the time can be used to adjust the operating parameters of the water pretreatment system in time, thereby ensuring the timeliness of the adjustment of the operating parameters.

[0096] In some embodiments, the preset sliding window step size can be set to 5 to 15 minutes, and the preset sliding window length can be set to 1 to 7 days. Specifically, the preset sliding window step size can be 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 minutes, and the preset sliding window length can be set to 1, 2, 3, 4, 5, 6, 7 days. The preset sliding window step size or the preset sliding window length can be adjusted according to the characteristics of the water inlet of the water pretreatment system, and can also be adjusted according to the operating characteristics of the water pretreatment system in actual application. The preset sliding window step size can also be set to less than 5 minutes or greater than 15 minutes, or the preset sliding window length can be set to greater than 7 days, without specific limitation.

[0097] In some embodiments, the water quality grayscale value at each historical moment in the training data can be obtained by using the water quality data at each historical moment to refer to the current water quality grayscale value y 0 It is calculated by the calculation formula, that is, the water quality data at each historical moment is obtained, then standardized, and finally calculated using the standardized data and the weight parameters of each type of water quality index.

[0098] In some embodiments, the current water quality grayscale value of the current influent water of the water pretreatment system can be calculated by two different methods to obtain two values. Method 1: Calculate the current water quality data of the current influent water of the water pretreatment system, that is, calculate the current water quality data of the water pretreatment system by the following formula:

[0099]

[0100] The current water quality grayscale value calculated by method one can be called the real-time water quality grayscale value. Method two is predicted according to the grayscale value prediction model, and the predicted current water quality grayscale value can be called the predicted water quality grayscale value. In general, the operation decision step is performed according to the real-time water quality grayscale value to determine the operation parameters of the water pretreatment system. However, in the actual application process, the detection equipment that detects the water quality data of the current inflow of the water pretreatment system may have a detection error, and the real-time water quality grayscale value calculated thereby deviates from the actual water quality situation; it is also possible that the current inflow is only a short-term fluctuation, and this short-term fluctuation will not have a significant impact on the operating efficiency of the water pretreatment system, that is, in the face of such a short-term fluctuation, there is no need to adjust the operating parameters of the water pretreatment system. Therefore, in this method, the predicted water quality grayscale value obtained by prediction can be used to correct the real-time water quality grayscale value. When the real-time water quality grayscale value differs greatly from the predicted water quality grayscale value, it is considered that the water quality data detected by the detection equipment is wrong. At this time, the operation decision step is performed according to the predicted water quality grayscale value to determine the operation parameters of the water pretreatment system. Through the coordination between the predicted water quality grayscale value and the real-time water quality grayscale value, relevant staff can promptly discover problems with the front-end water quality testing equipment, and promptly repair and replace the testing equipment. At the same time, they can effectively respond to short-term fluctuations in water quality and water volume, and effectively ensure the normal operation of the water pretreatment system.

[0101] In some embodiments, the operating flux corresponding to the first parameter and the operating flux corresponding to the second parameter are calculated by the following formula: 0 =F×Q 1 / Q 0 ;

[0102] The operating flux corresponding to the third parameter is 0.7F to 0.9F, the operating flux corresponding to the fourth parameter is F, and the operating flux corresponding to the fifth parameter is 1.2F to 1.4F;

[0103] Among them, F 0 is the operating flux corresponding to the first parameter or the operating flux corresponding to the second parameter, F is the design flux of the water pretreatment system, Q 1 is the actual water volume, Q 0 The designed inlet water volume for the water pretreatment system.

[0104] At the beginning of the construction of the water pretreatment system, the operating flux and water volume will be designed according to factors such as the scale of the water plant, the type and characteristics of the water being treated, that is, the design flux F and the design inlet water volume Q are obtained. 0 After the water pretreatment system is put into actual use, it is generally based on the design flux F and the design water volume Q 0However, the water quality and quantity of water from the water plant usually fluctuate over time or seasons. The water pretreatment system has always been running at the same design flux F and design influent volume Q. 0 Operation can easily lead to problems such as low production efficiency and high operating costs.

[0105] In this method, the operating parameters of the water treatment system are adjusted according to the actual water volume. When the actual water volume is greater than the first threshold, or the actual water volume is less than the second threshold, the actual water volume Q 1 , design flux F and design water inflow Q 0 To adjust the corresponding operating parameters in real time, that is, according to F 0 =F×Q 1 / Q 0 To calculate the operating flux corresponding to the first parameter or the second parameter. When the actual water volume is less than or equal to the first threshold value and greater than or equal to the second threshold value, the operating parameter is judged according to the current water quality grayscale value. Specifically, the operating flux corresponding to the third operating parameter, the fourth operating parameter, and the fifth operating parameter can be determined according to the design flux F; when the current water quality level is the first level, it means that the water quality is good, and the water pretreatment system operates with an operating flux less than the design flux F, specifically, it operates with an operating flux of 0.7F to 0.9F, that is, the operating flux corresponding to the third parameter is 0.7F to 0.9F. Optionally, the operating flux corresponding to the third parameter can be 0.7F, 0.75F, 0.8F, 0.85F, 0.9F, etc., and can also be set to less than 0.7 according to actual conditions. F or greater than 0.9F, without specific limitation; when the current water quality level is the second level, it means that the water quality meets the general normal situation, and the water pretreatment system operates with the design flux F, that is, the operating flux corresponding to the fourth parameter is F; when the current water quality level is the third level, it means that the water quality is poor, and the water pretreatment system operates with an operating flux greater than the design flux F, specifically, it operates with an operating flux of 1.2F~1.4F, that is, the operating flux corresponding to the fifth parameter is 1.2F~1.4F, optionally, the operating flux corresponding to the fifth parameter can be 1.2F, 1.25F, 1.3F, 1.35F, 1.4F, etc., and can also be set to less than 1.2F or greater than 1.4F according to actual conditions, without specific limitation.

[0106] In some embodiments, the first threshold or the second threshold can be determined based on the designed water inflow volume Q 0 The first threshold can be set to be greater than the designed water inlet volume Q 0 Specifically, the first threshold is 1.1Q 0 The second threshold can be set to be less than the designed water inlet volume Q 0 Specifically, the second threshold is 0.9Q 0 , when the actual water volume Q 1>1.1Q 0 When the water pretreatment system operates with the first parameter, the actual water volume Q 1 <0.9Q 0 When the water pretreatment system operates with the second parameter, when 0.9Q 0 ≤Actual water volume Q 1 ≤1.1Q 0 When the current water quality gray value is used, the operation decision step is performed to determine the operation parameters of the water pretreatment system. Optionally, the first threshold and the second threshold can also be set to other values ​​according to actual conditions. For example, the first threshold can be 1.15Q 0 , 1.2Q 0 , 1.25Q 0 , 1.3Q 0 or other values ​​greater than Q 0 The second threshold value can be 0.85Q 0 , 0.8Q 0 , 0.75Q 0 , 0.7Q 0 or other values ​​less than Q 0 There is no specific limit on the value of .

[0107] In some embodiments, the operating parameters of the water pretreatment system can be adjusted every 0.5 to 2 hours. During the operation of the water pretreatment system, the actual amount of water entering the water changes in real time. Even in the stage of stable water inflow, the actual amount of water will fluctuate slightly, and the operating flux corresponding to the first parameter and the second parameter calculated thereby will also fluctuate slightly. At this time, whether it is an automated system or manual real-time adjustment, the operating cost is obviously high. Therefore, the operating parameters of the water pretreatment system can be set to be adjusted every 0.5 to 2 hours, and the average water inflow detected within 0.5 to 2 hours is used as the actual water volume. Specifically, it can be set to be adjusted once every 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.2, 1.5, 1.6, 1.8 or 2 hours, or it can be set to be adjusted once more than 2 hours or less than 0.5 hours, without specific restrictions. This ensures that the water pretreatment system operates effectively while improving economic benefits and further reducing operating costs.

[0108] In some embodiments, the operating parameters also include cleaning frequency; the cleaning frequency corresponding to the first parameter is greater than the cleaning frequency corresponding to the second parameter, and the cleaning frequency corresponding to the third parameter, the cleaning frequency corresponding to the fourth parameter, and the cleaning frequency corresponding to the fifth parameter increase successively.

[0109] The core component of the water pretreatment system is the filter component. When the membrane treatment system is used as the water pretreatment system to treat the incoming water, its filter component is the membrane component. Since the incoming water treated by the water pretreatment system usually contains some solid pollutants, the core component filter component needs to be cleaned after running for a period of time, otherwise it will cause serious membrane pollution, significant increase in energy consumption, and actual water quality decline. When cleaning the filter component of the water pretreatment system, it takes a certain amount of time, usually 0.5 hours. During the cleaning stage, the operation of the water pretreatment system needs to be suspended. Therefore, when the cleaning frequency is too high, it will take up more time, reducing the effective operation time of the water pretreatment, thereby reducing the total water output of the water pretreatment system, resulting in reduced production efficiency; when the cleaning frequency is too low, the filter component is seriously polluted, the filter hole is blocked, causing the porosity to decrease, thereby causing serious flux attenuation and increased filtration pressure, which will not only cause the water quality of the outlet to deteriorate, but also increase the operating energy consumption and reduce the service life of the filter component. Therefore, when adjusting the operating flux, adjusting to an appropriate cleaning frequency can not only ensure the operating efficiency of the water pretreatment system, but also ensure the water quality of the effluent and extend the service life of the filter components.

[0110] In this method, the operating parameters of the water pretreatment system also include the cleaning frequency. When the amount of incoming water is large or the quality of the incoming water is poor, the filter component is polluted quickly and the degree of pollution is serious. At this time, a higher cleaning frequency needs to be set to clean the filter component of the water pretreatment system, that is, the cleaning frequency corresponding to the first parameter is greater than the cleaning frequency corresponding to the second parameter, and the cleaning frequency corresponding to the third parameter, the cleaning frequency corresponding to the fourth parameter, and the cleaning frequency corresponding to the fifth parameter increase in sequence, thereby ensuring the effective operation of the water pretreatment system, effectively reducing the pollution of the filter component, increasing the service life, and further ensuring the stability of the outlet water volume and outlet water quality. Specifically, the water pretreatment system can be cleaned in the following ways:

[0111] (1) Alkali washing: A mixture of sodium hydroxide and sodium hypochlorite is transported to the water pretreatment system through a chemical cleaning pump, and then enters the filter assembly under the action of a circulating pump to clean the dirt on the filter assembly;

[0112] (2) Rinse with clean water: Rinse the filter assembly with clean water at least twice;

[0113] (3) Acid cleaning: Use citric acid to clean the filter components. The cleaning process is the same as that of alkaline cleaning. It is mainly used to clean the scale on the filter components.

[0114] (4) Rinse with clean water: Rinse the filter assembly with clean water at least twice to complete the cleaning of the filter assembly.

[0115] It should be noted that other cleaning methods, cleaning agents, alkaline cleaning agents, acid cleaning agents, etc. suitable for filter components can all be applied to this method. In actual application, the cleaning method, cleaning agent, etc. can be determined based on the actual conditions such as the characteristics of the filter components and the characteristics of the pollutants, without specific restrictions.

[0116] In some embodiments, the cleaning frequency corresponding to the first parameter and the cleaning frequency corresponding to the second parameter are calculated by the following formula: N 0 =1440 / T-60000Q 1 / FAT;

[0117] The cleaning frequency corresponding to the third parameter is 0.7N-0.9N, the cleaning frequency corresponding to the fourth parameter is N, and the cleaning frequency corresponding to the fifth parameter is 1.2N-1.4N;

[0118] Among them, N 0 is the cleaning frequency corresponding to the first parameter or the cleaning frequency corresponding to the second parameter, T is the single cleaning time, Q 1 is the actual water volume, F is the design flux of the water pretreatment system, A is the total filtration area of ​​the water pretreatment system, and N is the design cleaning frequency of the water pretreatment system.

[0119] At the beginning of the construction of the water pretreatment system, the cleaning frequency will be designed according to the scale of the water plant, the type and characteristics of the treated water, that is, the design cleaning frequency N is obtained. At the same time, according to the number and size of the filter components set, the total filtration area A can be calculated. After the water pretreatment system is put into use, the cleaning frequency is usually unchanged. However, when the water quality or water quantity fluctuates widely, the fixed cleaning frequency obviously cannot meet the actual needs.

[0120] In this method, when the actual water volume is greater than the first threshold, or the actual water volume is less than the second threshold, the actual water volume Q 1 , design cleaning frequency N, total filtration area A, single cleaning time T to calculate the corresponding cleaning frequency N 0 , that is, according to N 0 =1440 / T-60000Q 1 / FAT is used to calculate the cleaning frequency corresponding to the first parameter or the second parameter; when the actual water volume is less than or equal to the first threshold value and greater than or equal to the second threshold value, the cleaning frequency corresponding to the operating parameter is determined according to the current water quality grayscale value. Specifically, the cleaning frequencies corresponding to the third operating parameter, the fourth operating parameter, and the fifth operating parameter can be determined based on the designed cleaning frequency N; when the current water quality level is the first level, it means that the incoming water quality is good, there are fewer solid pollutants in the water, and the corresponding pollution to the filter component is also small. At this time, the water pretreatment system cleans the filter component with a cleaning frequency less than the designed cleaning frequency N, thereby extending the effective operation time of the water pretreatment system and effectively ensuring production efficiency. Specifically, the cleaning frequency is 0.7N~0.9N, that is, the cleaning frequency corresponding to the third parameter is 0.7N~0.9N. Optionally, the cleaning frequency corresponding to the third parameter can be 0.7N, 0.75N, 0.8N, 0.85N, 0.9N, etc., or According to the actual situation, it is set to be less than 0.7N or greater than 0.9N, without specific restrictions; when the current water quality level is the second level, it means that the water quality meets the general normal situation, and the water pretreatment system cleans the filter component with the designed cleaning frequency N, that is, the cleaning frequency corresponding to the fourth parameter is N; when the current water quality level is the third level, it means that the water quality is poor, there are more solid pollutants in the water, and the corresponding pollution to the filter component is also large. At this time, the water pretreatment system cleans the filter component with a cleaning frequency higher than the designed cleaning frequency N to ensure the normal operation of the water pretreatment system and the stability of the effluent water quality. Specifically, the filter component is cleaned with a cleaning frequency of 1.2N~1.4N, that is, the cleaning frequency corresponding to the fifth parameter is 1.2N~1.4N. Optionally, the cleaning frequency corresponding to the fifth parameter can be 1.2N, 1.25N, 1.3N, 1.35N, 1.4N, etc., and can also be set to less than 1.2N or greater than 1.4N according to actual conditions, without specific restrictions.

[0121] Daily water output of water pretreatment system Q 2 It can be calculated by the following formula:

[0122] Q 2 =(24-NT / 60)FA / 1000

[0123] It is not difficult to see from the above formula that the daily water output of the water pretreatment system will be affected by the cleaning frequency and operating flux. When the operating flux is too high or the water quality is poor, the filter components are more contaminated and need to be cleaned more frequently. However, since cleaning requires suspending the operation of the water pretreatment system and reducing the effective operating time of the water pretreatment system, excessively high-frequency cleaning will lead to a decrease in daily water output, thereby affecting the treatment efficiency of the water pretreatment system. Therefore, in this method, the operating flux and cleaning frequency of the water pretreatment system are determined by the actual water volume and the current water quality grayscale value. When the actual water volume is greater than the first threshold, the operating flux and the cleaning frequency are increased at the same time. When the actual water volume is less than the second threshold, the operating flux and the cleaning frequency are decreased at the same time. When the actual water volume is less than or equal to the first threshold and greater than or equal to the second threshold, the operating flux and the cleaning frequency are determined by the current water quality grayscale value, and the specific parameters of the two are determined according to the design amount of the water pretreatment system, that is, the operating flux is determined according to the design flux, and the cleaning frequency is determined according to the design cleaning frequency, thereby ensuring that the operating flux and the cleaning frequency can reasonably cooperate with each other, ensuring the daily water production of the water pretreatment system, and ensuring the stability of the effluent water quality.

[0124] In some embodiments, the multiple types of water quality indicators are determined according to the type of water influent to the water pretreatment system;

[0125] When the water inlet of the water pretreatment system is tap water, the multiple water quality indicators include water temperature, COD, turbidity and TOC; when the water inlet of the water pretreatment system is municipal sewage, the multiple water quality indicators include water temperature, COD, ammonia nitrogen and turbidity; when the water inlet of the water pretreatment system is anaerobic ammonia oxidation treated water, the multiple water quality indicators include water temperature, COD, ammonia nitrogen and SS.

[0126] COD (Total Oxygen Demand) is the total oxygen demand, which generally refers to the amount of oxygen required to oxidize the total carbon, hydrogen, nitrogen and a small amount of sulfur in the water. It reflects the content of organic matter, nitrite, etc. in the water. The larger the COD value, the more organic pollutants there are in the water and the more serious the pollution. TOC (Total Organic Carbon) is the total organic carbon, which is the total amount of carbon contained in dissolved and suspended organic matter in the water. It is used to reflect the degree of organic pollution to the water. Turbidity is used to characterize the transparency of water, usually caused by tiny particles such as silt, clay, microorganisms, organic matter and other substances. Ammonia nitrogen refers to ammonia (NH 3 ) and free amino ions (NH 4 +) is used to measure the degree of nitrogen pollution in water bodies. High concentrations of ammonia nitrogen will lead to eutrophication of water bodies and damage the water ecological environment. SS (suspended solid) is solid suspended matter, which refers to the concentration of solid matter suspended in water. Water temperature refers to the temperature of the water body, which will affect the growth of aquatic organisms and microorganisms. In this method, the higher the measured values ​​of water temperature, COD, turbidity, TOC, ammonia nitrogen and SS indicators, the worse the water quality. Conversely, the lower the measured values, the better the water quality.

[0127] Water pretreatment systems are used in a variety of water treatment processes, such as tap water supply, municipal sewage treatment, industrial sewage treatment, etc. In different water treatment processes, the characteristics of the treated water are quite different. For example, when used in tap water supply, the water inlet of the water pretreatment system is tap water raw water, which is usually collected from rivers, lakes, and has relatively stable water quality. The pollutants contained are mainly suspended solids and organic matter. Therefore, the four water quality indicators of water temperature, COD, turbidity and TOC are used to characterize the comprehensive water quality, that is, the detection data of the four water quality indicators of water temperature, COD, turbidity and TOC are used to calculate the current water quality gray value. When applied in the field of municipal sewage treatment, such as when treating water reuse water, the treated water is mainly municipal sewage, and the water quality is relatively stable, but turbid, and the nitrogen and phosphorus content is high. Therefore, the four water quality indicators of water temperature, COD, ammonia nitrogen and turbidity are used to characterize its comprehensive water quality, that is, the detection data of the four water quality indicators of water temperature, COD, ammonia nitrogen and turbidity are used to calculate the current water quality gray value. When applied in industrial sewage treatment, such as when treating anaerobic ammonia oxidation water such as restaurant biogas, garbage leachate, sludge nitrification supernatant, and leather wastewater, the contents of COD, ammonia nitrogen, and suspended particles in these waters are all high. Therefore, the four water quality indicators of water temperature, COD, ammonia nitrogen and SS are used to characterize its comprehensive water quality, that is, the detection data of the four water quality indicators of water temperature, COD, ammonia nitrogen and SS are used to calculate the current water quality gray value.

[0128] In some embodiments, the water quality level is obtained according to the following steps:

[0129] Acquire water quality data of water entering the water pretreatment system at multiple historical moments, wherein the water quality data includes detection data of multiple types of water quality indicators, and obtain parameters of the multiple types of water quality indicators at multiple historical moments;

[0130] Based on the maximum and minimum value method, the parameters of the multiple types of water quality indicators at the multiple historical moments are respectively standardized in the preset intervals to obtain the standardized parameters of the multiple types of water quality indicators at the multiple historical moments;

[0131] The weight parameters of each type of water quality index are determined based on the importance of water quality index using the analytic hierarchy process.

[0132] Calculate the water quality grayscale value at each historical moment by using the weight parameter of each type of water quality index and the standardized parameters of multiple types of water quality index at each historical moment, and obtain the water quality grayscale values ​​at multiple historical moments;

[0133] Determine the grouping breakpoints of the water quality grayscale values ​​at the multiple historical moments using the natural breakpoint method based on the number of water quality grade classifications to obtain a first breakpoint A and a second breakpoint B;

[0134] Determine that the water quality grayscale value ranges corresponding to the first level, the second level, and the third level are [0, A), [A, B), and [B, 1] respectively.

[0135] The water quality grade can be calculated based on the historical water quality data of the water pretreatment system. First, the parameters of multiple types of water quality indicators at multiple historical moments are obtained based on the water quality data of the water pretreatment system at multiple historical moments. Then, based on the maximum and minimum value method, the parameters of multiple types of water quality indicators at multiple historical moments are standardized in the preset interval to obtain the standardized parameters of multiple types of water quality indicators at multiple historical moments. Before standardization, the parameters of multiple types of water quality indicators at multiple historical moments can be classified according to the water quality indicators, that is, the parameters of multiple historical moments of each water quality indicator can be obtained, and then the maximum value and minimum value of the parameters of multiple historical moments of each water quality indicator are found to obtain the actual maximum value and the actual minimum value, and then the actual maximum value is standardized to the maximum value in the preset interval, and the actual minimum value is standardized to the minimum value in the preset interval. For example, when the preset interval is [0,1], the actual maximum value can be standardized to 1, and 1 is the standardized parameter of the actual maximum value, and the actual minimum value can be standardized to 0, and 0 is the standardized parameter of the actual minimum value, and then other values ​​between the actual maximum value and the actual minimum value are standardized to between 0 and 1, thereby obtaining the corresponding standardized parameters. Repeat the above operation for each type of water quality index to obtain the standardized parameters of multiple historical moments corresponding to each type of water quality index. Classify according to multiple historical moments to obtain the standardized parameters of multiple types of water quality indexes at multiple historical moments. The actual maximum and minimum values ​​obtained in the above process can be applied to the current water quality gray value y 0 In the calculation process, that is, in the process of standardizing the parameters of the current multiple categories of water quality indicators in the preset interval by using the maximum and minimum method, the actual maximum value and the actual minimum value are mapped to the maximum value and the minimum value in the preset interval [0,1], and then the parameters of the current multiple categories of water quality indicators are mapped to the preset interval [0,1] according to the actual maximum value and the actual minimum value, thereby obtaining the standardized parameters of the current multiple categories of water quality indicators.

[0136] In this method, multiple types of water quality indicators are selected to characterize the comprehensive water quality situation, but the degree of influence of each water quality indicator on the water quality situation is different. According to different application scenarios, water pretreatment purposes, etc., the importance of water quality indicators will also vary. Therefore, it is necessary to use the analytic hierarchy process to determine the weight parameters of each type of water quality indicator based on the importance of water quality indicators, and then calculate the water quality gray value at each historical moment according to the weight parameters of each type of water quality indicator and the standardized parameters of multiple types of water quality indicators at each historical moment, and obtain the water quality gray values ​​at multiple historical moments. Finally, according to the number of water quality grade classifications, the natural breakpoint method is used to determine the grouping breakpoints of the water quality gray values ​​at multiple historical moments, and the first breakpoint A and the second breakpoint B are obtained, thereby determining the water quality gray value ranges corresponding to the first level, the second level and the third level are [0, A), [A, B), [B, 1] respectively. Optionally, the analytic hierarchy process can be implemented using software such as SPSSPRO, MATLAB, LINGO or Yaahp, and other software and platforms that can implement the analytic hierarchy process can be applied to this method, without specific limitation.

[0137] The following uses the treatment of anaerobic ammonium oxidation treated water as an example to illustrate the steps for classifying water quality.

[0138] When processing the liquid, four water quality indicators, namely water temperature, COD, ammonia nitrogen and SS, are selected. The water quality data of a certain month are selected to calculate the water quality grade. In the water quality data of this month, each water quality data is collected at intervals of 1 hour, and each water quality data includes the detection data corresponding to water temperature, COD, ammonia nitrogen and SS, that is, the parameters of the four types of water quality indicators at multiple historical moments in a month can be obtained. For each type of water quality indicator, there are multiple parameters at historical moments. Using the maximum and minimum method, the parameters of multiple historical moments of each type of water quality indicator are standardized to the [0,1] interval to obtain the corresponding standardized parameters, that is, the standardized parameters of multiple historical moments of water temperature, the standardized parameters of multiple historical moments of COD, the standardized parameters of multiple historical moments of ammonia nitrogen, and the standardized parameters of multiple historical moments of SS are obtained respectively. The standardized parameters of multiple historical moments of water temperature, the standardized parameters of multiple historical moments of COD, the standardized parameters of multiple historical moments of ammonia nitrogen, and the standardized parameters of multiple historical moments of SS are combined to obtain the standardized parameters of multiple types of water quality indicators at multiple historical moments. At the same time, for each historical moment, there are standardized parameters of four types of water quality indicators: water temperature, COD, ammonia nitrogen and SS. Therefore, the water quality grayscale value corresponding to each historical moment can be calculated according to the standardized parameters of water temperature, COD, ammonia nitrogen and SS and the weight parameters of water temperature, COD, ammonia nitrogen and SS, that is, the water quality grayscale values ​​of multiple historical moments are obtained. The water quality grayscale value of each historical moment can be calculated by referring to the calculation formula of the current water quality grayscale value, that is, it is calculated by the following formula:

[0139]

[0140] Among them, y t is the gray value of water quality at historical time t, X norm_i_t It is the standardized parameter of the i-th category water quality indicator at historical moment t.

[0141] Finally, the natural breakpoint method is used to determine the grouping breakpoints of the water quality grayscale values ​​at multiple historical moments, and the first breakpoint A and the second breakpoint B are obtained, so as to determine that the water quality grayscale value ranges corresponding to the first level, the second level, and the third level are [0, A), [A, B), and [B, 1] respectively.

[0142] In some embodiments, before obtaining the standardized parameters of multiple types of water quality indicators at multiple historical moments, it also includes: removing outliers. Optionally, 3 times the standard deviation can be used to remove outliers in the water quality data. First, for the water quality indicator X, calculate the standard deviation σ and the arithmetic mean of its time series data Secondly, search for X that does not belong to The value of the interval is determined to be an abnormal value and deleted. Here, if there is an abnormality in at least one water quality index in the water quality data at the same historical moment, the entire data is deleted.

[0143] In some embodiments, the method of determining the weight parameter of each type of water quality indicator based on the importance of the water quality indicator using the hierarchical analysis method includes:

[0144] The multiple types of water quality indicators are grouped in pairs to compare their importance, and the relative importance of each two types of water quality indicators is determined according to the preset scoring criteria to obtain multiple importance scores;

[0145] Constructing a judgment matrix using the multiple importance scores;

[0146] Based on the judgment matrix, the maximum eigenvalue and eigenvector corresponding to the judgment matrix are calculated;

[0147] Performing a consistency check on the judgment matrix using the maximum eigenvalue;

[0148] In response to determining that the judgment matrix meets the consistency test requirement, using the characteristic vector corresponding to the current judgment matrix to obtain the weight parameter of each type of water quality index;

[0149] In response to determining that the judgment matrix does not meet the consistency check requirements, the judgment matrix is ​​reconstructed to obtain a new judgment matrix, a new maximum eigenvalue and a new eigenvector are calculated based on the new judgment matrix, and the new maximum eigenvalue is used to perform a consistency check on the new judgment matrix until the new judgment matrix meets the consistency check requirements.

[0150] The scoring criteria are as follows:

[0151] Comparison of the relative importance of two types of water quality indicators Importance score Equally important 1 Slightly important 3 Strong and important 5 Strongly important 7 Extremely important 9 The middle value of two adjacent judgments 2，4，6，8

[0152] When comparing the i-th water quality index X j and the jth water quality index X j When the importance of water quality index X i Water quality index X j If they are equally important, they can get an importance score a ij is 1, if the water quality index X i Water quality index X j Slightly important, it can get an importance score of a ij =2, thus multiple water quality indicators are grouped and compared in pairs to obtain multiple importance scores. Then, a judgment matrix Z can be constructed through multiple importance scores, and the maximum eigenvalue λ can be calculated based on the judgment matrix Z. max and the eigenvector W.

[0153] Using the maximum eigenvalue λ max The specific steps for consistency check of judgment matrix Z are as follows:

[0154] Using the maximum eigenvalue λ max The consistency index CI is calculated according to the following formula:

[0155]

[0156] When CI = 0, it means that the judgment matrix is ​​completely consistent, CI is close to 0, the consistency is satisfactory, and the larger the CI, the more serious the inconsistency of the judgment matrix. In order to measure the size of the consistency index CI, the random consistency index RI is introduced, and the test coefficient CR is calculated according to the following formula:

[0157]

[0158]

[0159] If CR<0.1, it is considered that the judgment matrix Z meets the consistency test requirements, and the eigenvector W corresponding to the judgment matrix Z is used to obtain the weight parameter w of each type of water quality index. Otherwise, it does not meet the consistency test requirements. At this time, it is necessary to reconstruct the judgment matrix to obtain a new judgment matrix Z′, and then perform a consistency test on the new judgment matrix Z′ until the new judgment matrix Z′ meets the consistency test requirements. The weight parameters of each type of water quality index obtained in the above process can be applied to the current water quality gray value y 0 's calculation.

[0160] In some embodiments, the determining the grouping breakpoints of the water quality grayscale values ​​at the multiple historical moments using the natural breakpoint method based on the number of water quality grade classifications to obtain the first breakpoint A and the second breakpoint B comprises:

[0161] Based on the number of water quality grade classifications, a grouping method of water quality grayscale values ​​at multiple historical moments is arbitrarily determined to obtain a current grouping method;

[0162] Calculate the sum of squares of deviations of the array mean and the sum of squares of deviations of the category mean corresponding to the current grouping method;

[0163] The variance goodness of fit corresponding to the current grouping method is calculated based on the sum of squares of deviations of the array mean and the sum of squares of deviations of the category mean;

[0164] In response to determining that the variance goodness of fit meets the preset condition, determining the grouping breakpoint in the current grouping method as the final grouping breakpoint, that is, obtaining the first breakpoint A and the second breakpoint B;

[0165] In response to determining that the variance goodness of fit does not meet the preset conditions, a grouping method is re-determined to obtain a new grouping method, and the variance goodness of fit corresponding to the new grouping method is calculated until the variance goodness of fit corresponding to the new grouping method meets the preset conditions.

[0166] In this method, the water quality level is divided into the first level, the second level, and the third level, with a total of three levels, so the number of levels is 3. The water quality grayscale values ​​at multiple historical moments are randomly divided into 3 groups, and the corresponding array mean deviation sum of squares SDAM and category mean deviation sum of squares SDCM are calculated according to the following formula:

[0167]

[0168]

[0169] in, is the average value of all water quality grayscale indicators, y k is the kth water quality gray value; is the average value of all water quality gray values ​​in the mth group, y m_g is the g-th water quality grayscale value in the m-th group. For example, suppose there are 10 water quality grayscale values ​​at historical moments that need to be grouped. is the average grayscale value of water quality at 10 historical moments, y k Then it is the kth water quality grayscale value among the water quality grayscale values ​​at 10 historical moments. Group the water quality grayscale values ​​at 10 historical moments. If there are three water quality grayscale values ​​in the mth group, they are y 1 ,y 2 ,y 3 , is the average value of the three water quality gray values ​​in the mth group, that is, y 1 ,y 2 ,y 3 For each group of water quality grayscale values ​​in the classification, the average value of the water quality grayscale values ​​in a group can be calculated, and then the sum of squared deviations of the array average value can be calculated based on the water quality grayscale values ​​in the corresponding group.

[0170] The variance goodness of fit GVF corresponding to the current grouping method is calculated according to the following formula:

[0171]

[0172] Generally speaking, the value of GVF falls between 0 and 1, 1 indicates an extremely good fit, and 0 indicates an extremely poor fit. Usually, when GVF>0.7, the corresponding grouping method is considered to be more reasonable; if GVF≤0.7, the corresponding grouping method is considered to be more unreasonable, and it is necessary to re-group and calculate the corresponding variance goodness of fit. Optionally, the preset condition in this method is greater than 0.7. When the calculated variance goodness of fit is greater than 0.7, it means that the current grouping method is more reasonable, and the grouping breakpoint in the current grouping method is determined as the final grouping breakpoint, and the first breakpoint A and the second breakpoint B are obtained.

[0173] In this method, when a variance goodness of fit is calculated to be greater than 0.7, the breakpoint of the corresponding grouping method is determined as the final grouping breakpoint, that is, the first breakpoint A and the second breakpoint B are obtained. The grayscale value ranges of the first, second and third levels of water quality obtained are more reasonable. At the same time, there is no need to traverse all grouping methods, the calculation efficiency is high, and the demand for computing resources of the equipment is low, which is conducive to practical application and promotion.

[0174] In some embodiments, the determining the grouping breakpoints of the water quality grayscale values ​​at the multiple historical moments using the natural breakpoint method based on the number of water quality grade classifications to obtain the first breakpoint A and the second breakpoint B comprises:

[0175] The water quality grayscale values ​​at multiple historical moments are grouped multiple times based on the number of water quality grade classifications to obtain all grouping methods;

[0176] Calculate the sum of squared deviations of the array mean and the sum of squared deviations of the category mean corresponding to all grouping methods;

[0177] The variance goodness of fit corresponding to each grouping method is calculated based on the sum of squares of deviations of the corresponding array mean and the sum of squares of deviations of the category mean;

[0178] The grouping method corresponding to the maximum variance goodness of fit that meets the preset conditions is determined as the optimal grouping method, and the grouping breakpoints in the optimal grouping method are determined as the final grouping breakpoints, to obtain the first breakpoint A and the second breakpoint B.

[0179] The first breakpoint A and the second breakpoint B can be obtained by traversing all grouping methods. First, all possible grouping methods are determined to obtain all grouping methods, and then the corresponding variance goodness of fit is calculated for each grouping method, and the grouping method corresponding to the maximum variance goodness of fit greater than 0.7 is determined to be the best grouping method. For example, if there are 3 variance goodness of fit greater than 0.7, the grouping method corresponding to the maximum variance goodness of fit is determined to be the best grouping method, and finally the grouping breakpoint in the best grouping method is determined to be the final grouping breakpoint, and the first breakpoint A and the second breakpoint B can be obtained. In this method, since all grouping methods are traversed, the first breakpoint A and the second breakpoint B obtained are the best breakpoints, and the grayscale value ranges of the first, second and third levels of water quality obtained are the most reasonable ranges.

[0180] In some embodiments, the natural breakpoint method can be implemented using the jenkspy program of the python programming language, that is, the water quality grade level and the water quality grayscale values ​​at multiple historical moments are input into the jenkspy program, and the first breakpoint A and the second breakpoint B can be output. Other software and programs that can implement the natural breakpoint method can also be applied to this method, and are not specifically limited.

[0181] In some embodiments, the water pretreatment system is a membrane treatment system;

[0182] Wherein, the membrane treatment system is a ceramic membrane water treatment system.

[0183] The membrane treatment system is a common water pretreatment system. Its filter components are made of membrane materials, collectively referred to as membrane components. By setting up membranes of different structures, it can achieve the separation of various substances such as solid-liquid, gas, gas-solid, etc. Among the existing membrane treatment systems, the membrane components of the ceramic membrane water treatment system are more resistant to pollution, have a long service life, and have broad application prospects. The membrane components of the ceramic membrane water treatment system are porous asymmetric membranes made of materials such as aluminum oxide, iron oxide, and zirconium oxide through special processes. Membranes of different pore sizes are selected according to the viscosity of the material, the content of suspended matter, and other properties, which can achieve the purpose of clarification and separation. Commonly used ceramic membrane water treatment systems include tubular cross-flow filtration and flat dead-end filtration. The flow rate and operating pressure of the water pump are adjusted by controlling the frequency of its inverter, thereby affecting the operating flux of the membrane component. When the membrane treatment system is used as a water pretreatment system to pretreat water, the total filtration area is the total area of ​​the membrane component.

[0184] The present disclosure provides a water pretreatment method, which adjusts the operating flux and cleaning frequency of the water pretreatment system through the actual water volume of the current influent water of the water pretreatment system and the current water quality grayscale value representing the comprehensive water quality of the current influent water. When the influent water volume and water quality fluctuate, it ensures that the water pretreatment system can always operate with a reasonable operating flux and cleaning frequency; when operating at a high operating flux, it is combined with a high cleaning frequency to clean the filter component in time to ensure the effluent water quality and effluent efficiency; when operating at a low operating flux, it is combined with a low cleaning frequency to reduce the operating load of the water pretreatment system, which is friendly to the filter component and prolongs the service life of the filter component. At the same time, because the cleaning frequency is reduced, the effective operation time of the water pretreatment system is increased, and the water production efficiency can be guaranteed at the same time. When the actual water volume is greater than the first threshold or less than the second threshold, the water volume fluctuates greatly, and the corresponding operating flux and cleaning frequency are adjusted according to the actual water volume. When the actual water volume is less than or equal to the first threshold and greater than or equal to the second threshold, the corresponding operating flux and cleaning frequency are determined according to the current water quality grayscale value, and are determined according to the design parameters, that is, the operating flux is determined according to the design flux, and the cleaning frequency is determined according to the design cleaning frequency, ensuring reasonable adjustment of the operating flux and cleaning frequency, and ensuring effective coordination between the two, thereby improving the operating efficiency of the water pretreatment system, reducing operating costs, increasing service life, ensuring stable effluent water quality, and facilitating on-site promotion and practical application.

[0185] The present disclosure also provides an anaerobic ammonium oxidation water treatment process, which uses the water pretreatment method of any of the above embodiments to pretreat water to be treated, and the pretreated water is passed into an anaerobic ammonium oxidation system for treatment.

[0186] Anaerobic ammonium oxidation process is a common wastewater treatment process. Under anaerobic or anoxic conditions, ammonia is used as an electron donor and nitrate or nitrite is used as an electron acceptor. Anaerobic ammonium oxidation bacteria are used to directly oxidize ammonia nitrogen into nitrogen gas to remove nitrogen from wastewater. It is mainly used to treat wastewater with high concentration of ammonia nitrogen, such as kitchen biogas, sludge nitrification supernatant, garbage filtrate, and leather wastewater. This process breaks through the basic theoretical concepts in traditional biological denitrification processes, saves more than 60% of oxygen supply compared to full nitrification (ammonia oxidation to nitrate), and can also save the carbon source required in traditional biological denitrification processes. Before using the anaerobic ammonium oxidation process to treat wastewater, since various solid substances, toxic substances, organic substances, etc. contained in the wastewater will affect the anaerobic ammonium oxidation reaction, it is necessary to pre-treat the wastewater entering the anaerobic ammonium oxidation system, which can be achieved by using a membrane treatment system. The ceramic membrane water treatment system, as a water pretreatment system with excellent performance, can also be applied to the anaerobic ammonium oxidation process to achieve pre-treatment of wastewater. Therefore, the existing anaerobic ammonium oxidation process water treatment circuit usually consists of pretreatment and anaerobic ammonium oxidation reaction. When the pretreatment cannot effectively cope with the fluctuation of influent water volume and water quality, it will further affect the efficiency of anaerobic ammonium oxidation reaction, resulting in low treatment efficiency of the entire anaerobic ammonium oxidation water treatment circuit and increased operating costs. Taking industrial wastewater such as kitchen biogas, sludge nitrification supernatant, garbage filtrate, and leather wastewater as anaerobic ammonium oxidation treated water as an example, the water quality and water volume of the above wastewater fluctuate significantly. Sometimes the influent volume is large and urgent, sometimes the influent volume is very small, sometimes the influent water quality is poor, and sometimes the influent water quality is good. There are obvious fluctuations in water quality and water volume. Therefore, when the existing anaerobic ammonium oxidation process treats this type of wastewater, the problem of large changes in water quality and water volume cannot be well dealt with in the previous pretreatment process, resulting in low treatment efficiency of the entire anaerobic ammonium oxidation process line, high operating costs, and significant damage to the filter components of the water pretreatment system. When the filter components are severely damaged, the effluent quality will be further reduced, and the change in water quality will further affect the growth of anaerobic ammonium oxidation bacteria in the anaerobic ammonium oxidation process, thereby further reducing the efficiency of the anaerobic ammonium oxidation reaction, which is not conducive to practical application and promotion.

[0187] The anaerobic ammonia oxidation water treatment process provided by the present method uses the water pretreatment method described in any of the aforementioned embodiments to pretreat the water to be treated during the pretreatment process, so that the water pretreatment system can timely adjust the operating flux and cleaning frequency according to the water volume and water quality of the front-end water, effectively ensuring the operating efficiency of the water pretreatment system. At the same time, it is friendly to the filter components, the effluent water quality is stable, and the smooth progress of the anaerobic ammonia oxidation reaction is effectively ensured.

[0188] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0189] In addition, when details are set forth to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the present application embodiments can be implemented without these details or with changes in these details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0190] For simplifying explanation and discussion, and in order not to make the embodiment of the present application difficult to understand, the known power supply / ground connection with other parts may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiment of the present application difficult to understand, and this also takes into account the following fact, that is, the details of the implementation method of these block diagram devices are highly dependent on the platform to be implemented in the embodiment of the present application (that is, these details should be completely within the scope of understanding of those skilled in the art). In the case of elaborating specific details to describe the exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiment of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0191] Although the present application has been described in conjunction with the specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. The present application embodiments are intended to encompass all such replacements, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application embodiments should be included in the protection scope of the present application.

Claims

1. A water pretreatment method, characterized in that: include: Obtain the actual water volume of the current influent water pretreatment system and the current water quality grayscale value representing the current comprehensive water quality of the influent water; Determining the operating parameters of the water pretreatment system using the actual water volume and the current water quality grayscale value specifically includes: In response to determining that the actual water volume is greater than a first threshold, the water pre-treatment system operates with a first parameter; In response to determining that the actual water volume is less than a second threshold, the water pretreatment system operates with a second parameter; In response to determining that the actual water volume is less than or equal to the first threshold and greater than or equal to the second threshold, an operation decision step is performed using the current water quality grayscale value; the operation decision step includes: Determine the water quality grade of the current water inflow of the water pretreatment system by using the current water quality gray value to obtain the current water quality grade; wherein the water quality grade is pre-divided according to the water quality gray value representing the comprehensive water quality situation, including the first grade, the second grade and the third grade representing the water quality situation getting worse in sequence; In response to determining that the current water quality level is the first level, the water pretreatment system operates with a third parameter; In response to determining that the current water quality level is the second level, the water pretreatment system operates with fourth parameters; In response to determining that the current water quality level is the third level, the water pretreatment system operates with a fifth parameter; Among them, the operating parameters include operating flux, the operating flux corresponding to the first parameter is greater than the operating flux corresponding to the second parameter, and the operating flux corresponding to the third parameter, the operating flux corresponding to the fourth parameter and the operating flux corresponding to the fifth parameter increase in sequence.

2. A water pretreatment method according to claim 1, characterized in that: The current water quality grayscale value is calculated based on the water quality data of the current influent of the water pretreatment system, specifically including: Acquire water quality data of current influent of the water pretreatment system, wherein the water quality data includes detection data of multiple types of water quality indicators, and obtain parameters of the current multiple types of water quality indicators; The parameters of the current multiple types of water quality indicators are respectively standardized in a preset range by using the maximum and minimum method to obtain the standardized parameters of the current multiple types of water quality indicators; The current water quality grayscale value is calculated based on the preset weight parameters of each type of water quality index and the standardized parameters of the current multiple types of water quality indexes; The current water quality grayscale value is calculated by the following formula: Where y0 is the current water quality grayscale value, n is the number of categories of water quality indicators, i∈[1,n], w i is the weight parameter of the i-th water quality index, X norm_i It is the standardized parameter of the current i-th category water quality indicator.

3. A water pretreatment method according to claim 1, characterized in that: The current water quality grayscale value is calculated by using a pre-built grayscale value prediction model using the water quality data of the influent before a preset time step of the water pretreatment system, specifically including: Acquire water quality data of influent water of the water pretreatment system before a preset time step, wherein the water quality data includes detection data of multiple types of water quality indicators, and obtain parameters of the multiple types of water quality indicators before the preset time step; Inputting the parameters of the multiple types of water quality indicators before the preset time step into the gray value prediction model to predict the current water quality gray value; The gray value prediction model is constructed using a long short-term memory neural network based on a sliding window, and specifically includes: Obtain water quality data of water inflow at multiple historical moments of the water pretreatment system and corresponding water quality grayscale values ​​at multiple historical moments, and record corresponding time series information to obtain training data; Processing the training data using a sliding window method based on a preset sliding window step size and a preset sliding window length to obtain a training sample set; Using the training sample set to perform prediction training on an initial model constructed based on a long short-term memory neural network based on a preset time step until a preset termination condition is reached; The model parameters at the time when the training is terminated are determined as final model parameters, and the final model parameters are applied to the initial model to obtain the gray value prediction model.

4. A water pretreatment method according to any one of claims 1 to 3, characterized in that: The operating flux corresponding to the first parameter and the operating flux corresponding to the second parameter are calculated by the following formula: F0=F×Q1 / Q0; The operating flux corresponding to the third parameter is 0.7F to 0.9F, the operating flux corresponding to the fourth parameter is F, and the operating flux corresponding to the fifth parameter is 1.2F to 1.4F; Among them, F0 is the operating flux corresponding to the first parameter or the operating flux corresponding to the second parameter, F is the design flux of the water pretreatment system, Q1 is the actual water volume, and Q0 is the design inlet water volume of the water pretreatment system.

5. A water pretreatment method according to any one of claims 1 to 3, characterized in that: The operating parameters also include cleaning frequency; the cleaning frequency corresponding to the first parameter is greater than the cleaning frequency corresponding to the second parameter, and the cleaning frequency corresponding to the third parameter, the cleaning frequency corresponding to the fourth parameter, and the cleaning frequency corresponding to the fifth parameter increase in sequence.

6. A water pretreatment method according to claim 5, characterized in that: The cleaning frequency corresponding to the third parameter is 0.7N-0.9N, the cleaning frequency corresponding to the fourth parameter is N, and the cleaning frequency corresponding to the fifth parameter is 1.2N-1.4N; Where N is the designed cleaning frequency of the water pretreatment system.

7. A water pretreatment method according to any one of claims 2 to 3, characterized in that: The multiple types of water quality indicators are determined according to the type of water inlet to the water pretreatment system; When the water inlet of the water pretreatment system is tap water, the multiple water quality indicators include water temperature, COD, turbidity and TOC; when the water inlet of the water pretreatment system is municipal sewage, the multiple water quality indicators include water temperature, COD, ammonia nitrogen and turbidity; when the water inlet of the water pretreatment system is anaerobic ammonia oxidation treated water, the multiple water quality indicators include water temperature, COD, ammonia nitrogen and SS.

8. A water pretreatment method according to any one of claims 1 to 3, characterized in that: The water quality grades are obtained according to the following steps: Acquire water quality data of water entering the water pretreatment system at multiple historical moments, wherein the water quality data includes detection data of multiple types of water quality indicators, and obtain parameters of the multiple types of water quality indicators at multiple historical moments; Based on the maximum and minimum value method, the parameters of the multiple types of water quality indicators at the multiple historical moments are respectively standardized in the preset intervals to obtain the standardized parameters of the multiple types of water quality indicators at the multiple historical moments; The weight parameters of each type of water quality index are determined based on the importance of water quality index using the analytic hierarchy process. Calculate the water quality grayscale value at each historical moment by using the weight parameter of each type of water quality index and the standardized parameters of multiple types of water quality index at each historical moment, and obtain the water quality grayscale values ​​at multiple historical moments; Determine the grouping breakpoints of the water quality grayscale values ​​at the multiple historical moments using the natural breakpoint method based on the number of water quality grade classifications to obtain a first breakpoint A and a second breakpoint B; Determine that the water quality grayscale value ranges corresponding to the first level, the second level, and the third level are [0, A), [A, B), and [B, 1] respectively.

9. A water pretreatment method according to any one of claims 1 to 3, characterized in that: The water pretreatment system is a membrane treatment system; Wherein, the membrane treatment system is a ceramic membrane water treatment system.

10. An anaerobic ammonium oxidation water treatment process, characterized in that: The water to be treated is pretreated using a water pretreatment method as described in any one of claims 1 to 9, and the pretreated water is passed into an anaerobic ammonia oxidation system for treatment.

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